DolphinScheduler容错源码分析之Worker

2023-02-07 10:30 开发作者： leo的跟班

引言

上一篇文章介绍了DolphinScheduler中Master的容错机制，作为去中心化的多Master和多Worker服务对等架构，Worker的容错机制也是我们需要关注的。

和Master一样源码的版本基于3.1.3

Worker容错源码分析

worker启动注册

首先Worker的启动入口是在WorkerServer中，在Worker启动后就会执行其run方法

@PostConstruct
public void run() {
	this.workerRpcServer.start();
	this.workerRpcClient.start();
	this.taskPluginManager.loadPlugin();
	this.workerRegistryClient.setRegistryStoppable(this);
	this.workerRegistryClient.start();
	this.workerManagerThread.start();
	this.messageRetryRunner.start();
	/*
	 * registry hooks, which are called before the process exits
	 */
	Runtime.getRuntime().addShutdownHook(new Thread(() -> {
		if (!ServerLifeCycleManager.isStopped()) {
			close("WorkerServer shutdown hook");
		}
	}));
}

这里我们只关心this.workerRegistryClient.start();方法所做的事情：注册当前worker信息到Zookeeper，并且启动了一个心跳任务定时更新worker的信息到Zookeeper。

/**
 * registry
 */
private void registry() {
	WorkerHeartBeat workerHeartBeat = workerHeartBeatTask.getHeartBeat();
	String workerZKPath = workerConfig.getWorkerRegistryPath();
	// remove before persist
	registryClient.remove(workerZKPath);
	registryClient.persistEphemeral(workerZKPath, jsONUtils.toJsonString(workerHeartBeat));
	log.info("Worker node: {} registry to ZK {} successfully", workerConfig.getWorkerAddress(), workerZKPath);
	while (!registryClient.checkNodeExists(workerConfig.getWorkerAddress(), NodeType.WORKER)) {
		ThreadUtils.sleep(SLEEP_TIME_MILLIS);
	}
	// sleep 1s, waiting master failover remove
	ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
	workerHeartBeatTask.start();
	log.info("Worker node: {} registry finished", workerConfig.getWorkerAddress());
}

这里和master的注册流程基本一致，来看看worker注册的目录：

worker注册到zk的路径如下，并且和master都有相同的父级目录名称是/node：

// /nodes/worker/+ip:listenPortworkerConfig.setWorkerRegistryPath(REGISTRY_DOLPHINSCHEDULER_WORKERS + "/" + workerConfig.getWorkerAddress());

注册的内容就是当前worker节点的健康状况，包含了cpu，内存，负载，磁盘等信息，通过这些信息就可以标识当前worker是否健康，可以接收任务的分配并且去执行。

@Override
public WorkerHeartBeat getHeartBeat() {
	double loadAverage = OSUtils.loadAverage();
	double cpuUsage = OSUtils.cpuUsage();
	int maxCpuLoadAvg = workerConfig.getMaxCpuLoadAvg();
	double reservedMemory = workerConfig.getReservedMemory();
	double availablePhysicalMemorySize = OSUtils.availablePhysicalMemorySize();
	int execThreads = workerConfig.getExecThreads();
	int workerWaitingTaskCount = this.workerWaitingTaskCount.get();
	int serverStatus = getServerStatus(loadAverage, maxCpuLoadAvg, availablePhysicalMemorySize, reservedMemory,
			execThreads, workerWaitingTaskCount);
	return WorkerHeartBeat.builder()
			.startupTime(ServerLifeCycleManager.getServerStartupTime())
			.reportTime(System.currentTimeMillis())
			.cpuUsage(cpuUsage)
			.loadAverage(loadAverage)
			.availablePhysicalMemorySize(availablePhysicalMemorySize)
			.maxCpuloadAvg(maxCpuLoadAvg)
			.memoryUsage(OSUtils.memoryUsage())
			.reservedMemory(reservedMemory)
			.diskAvailable(OSUtils.diskAvailable())
			.processId(processId)
			.workerHostWeight(workerConfig.getHostWeight())
			.workerWaitingTaskCount(this.workerWaitingTaskCount.get())
			.workerExecThreadCount(workerConfig.getExecThreads())
			.serverStatus(serverStatus)
			.build();
}

Master监听worker在zk节点的状态

接下来，master就会对注册的worker节点进行监控，在上一篇的介绍中，master启动注册后对node节点已经进行了监听，大家可以进行回顾一下，这里监听了/node/节点，当其下面的子路径/master或者/worker有变动就会触发回调：

//node
registryClient.subscribe(REGISTRY_DOLPHINSCHEDULER_NODE, new MasterRegistryDataListener());

因此当worker临时节点异常后，master就会感知到其变化。最终会回调MasterRegistryDataListener中的notify方法，并根据变动的路径来判断是master还是worker：

@Override
public void notify(Event event) {
	final String path = event.path();
	if (Strings.isNullOrEmpty(path)) {
		return;
	}
	//monitor master
	if (path.startsWith(REGISTRY_DOLPHINSCHEDULER_MASTERS + Constants.SINGLE_SLASH)) {
		handleMasterEvent(event);
	} else if (path.startsWith(REGISTRY_DOLPHINSCHEDULER_WORKERS + Constants.SINGLE_SLASH)) {
		//monitor worker
		handleWorkerEvent(event);
	}
}

这段代码在之php前master的容错中也见到过。这里是对于worker的容错，就会触发handleWorkerEvent方法。

private void handleWorkerEvent(Event event) {
	final String path = event.path();
	switch (event.type()) {
		case ADD:
			logger.info("worker node added : {}", path);
			break;
		case REMOVE:
			logger.info("worker node deleted : {}", path);
			masterRegistryClient.removeWorkerNodePath(path, NodeType.WORKER, true);
			break;
		default:
			break;
	}
}

接下来就是获取到下线worker节点的host信息进行进一步的容错处理了：

public void removeWorkerNodePath(String path, NodeType nodeType, boolean failover) {
	logger.info("{} node deleted : {}", nodeType, path);
	try {
                //获取节点信息
		String serverHost = null;
		if (!StringUtils.isEmpty(path)) {
			serverHost = registryClient.getHostByEventDataPath(path);
			if (StringUtils.isEmpty(serverHost)) {
				logger.error("server down error: unknown path: {}", path);
				return;
			}
			if (!registryClient.exists(path)) {
				logger.info("path: {} not exists", path);
			}
		}
		// failover server
		if (failover) {
			failoverService.failoverServerWhenDown(serverHost, nodeType);
		}
	} catch (Exception e) {
		logger.error("{} server failover failed", nodeType, e);
	}
}

整个worker容错的大致过程如下：

1-获取需要容错worker节点的启动时间，用于后续判断worker节点是否还在下线状态，或者是否已经重新启动

2-根据异常的worker的信息查询需要容错的任务实例，获取只属于当前master节点需要容错的任务实例信息，这里也是和master不同的，并且容错没加锁的原因。

3-遍历所有要容错的任务实例进行容错这里注意的是需要容错的任务是在worker重新启动之前的任务，之后worker异常重启后分配的新任务不要容错

/**
 * Do the worker failover. Will find the SUBMITTED_SUCCESS/DISPATCH/RUNNING_EXECUTION/DELAY_EXECUTION/READY_PAUSE/READY_STOP tasks belong the given workerandroid,
 * and failover these tasks.
 * <p>
 * Note: When we do worker failover, the master will only failover the processInstance belongs to the current master.
 *
 * @param workerHost worker host
 */
public void failoverWorker(@NonNull String workerHost) {
	LOGGER.info("Worker[{}] failover starting", workerHost);
	final StopWatch failoverTimeCost = StopWatch.createStarted();
	//获取需要容错worker节点的启动时间，用于后续判断worker节点是否还在下线状态，或者是否已经重新启动
	// we query the task instance from cache, so that we can directly update the cache
	final Optional<Date> needFailoverWorkerStartTime =
			getServerStartupTime(registryClient.getServerList(NodeType.WORKER), workerHost);
	//根据异常的worker的信息查询需要容错的任务实例，获取只属于当前master节点需要容错的任务实例信息，这里也是和master不同的，并且容错没加锁的原因。
	final List<TaskInstance> needFailoverTaskInstanceList = getNeedFailoverTaskInstance(workerHost);
	if (CollectionUtils.isEmpty(needFailoverTaskInstanceList)) {
		LOGGER.info("Worker[{}] failover finished there are no taskInstance need to failover", workerHost);
		return;
	}
	LOGGER.info(
			"Worker[{}] failover there are {} taskInstance may need to failover, will do a deep check, taskInstanceIds: {}",
			workerHost,
			needFailoverTaskInstanceList.size(),
			needFailoverTaskInstanceList.stream().map(TaskInstance::getId).collect(Collectors.toList()));
	final Map<Integer, ProcessInstance> processInstanceCacheMap = new HashMap<>();
	for (TaskInstance taskInstance : needFailoverTaskInstanceList) {
		LoggerUtils.setWorkflowAndTaskInstanceIDMDC(taskInstance.getProcessInstanceId(), taskInstance.getId());
		try {
			ProcessInstance processInstance = processInstanceCacheMap.computeIfAbsent(
					taskInstance.getProcessInstanceId(), k -> {
						WorkflowExecuteRunnable workflowExecuteRunnable = cacheManager.getByProcessInstanceId(
								taskInstance.getProcessInstanceId());
						if (workflowExecuteRunnable == null) {
							return null;
						}
						return workflowExecuteRunnable.getProcessInstance();
					});
			//这里注意的是需要容错的任务是在worker重新启动之前的任务，之后worker异常重启后分配的新任务不要容错
			if (!checkTaskInstanceNeedFailover(needFailoverWorkerStartTime, processInstance, taskInstance)) {
				LOGGER.info("Worker[{}] the current taskInstance doesn't need to failover", workerHost);
				continue;
			}
			LOGGER.info(
					"Worker[{}] failover: begin to failover taskInstance, will set the status to NEED_FAULT_TOLERANCE",
					workerHost);
			failoverTaskInstance(processInstance, taskInstance);
			LOGGER.info("Worker[{}] failover: Finish failover taskInstance", workerHost);
		} catch (Exception ex) {
			LOGGER.info("Worker[{}] failover taskInstance occur exception", workerHost, ex);
		} finally {
			LoggerUtils.removeWorkflowAndTaskInstanceIdMDC();
		}
	}
	failoverTimeCost.stop();
	LOGGER.info("Worker[{}] failover finished, useTime:{}ms",
			workerHost,
			failoverTimeCost.getTime(TimeUnit.MILLISECONDS));
}

4-更新taskInstance的状态为TaskExecutionStatus.NEED_FAULT_TOLERANCE。并且构造TaskStateEvent事件，设置其状态为需要容TaskExecutionStatus.NEED_FAULT_TOLERANCE的，其类型是TASK_STATE_CHANGE。最后提交需要容错的event。

private void failoverTaskInstance(@NonNull ProcessInstance processInstance, @NonNull TaskInstance taskInstance) {
	TaskMetrics.incTaskInstanceByState("failover");
	boolean isMasterTask = TaskProcessorFactory.isMasterTask(taskInstance.getTaskType());
	taskInstance.setProcessInstance(processInstance);
	if (!isMasterTask) {
		LOGGER.info("The failover taskInstance is not master task");
		TaskExecutionContext taskExecutionContext = TaskExecutionContextBuilder.get()
				.buildTaskInstanceRelatedInfo(taskInstance)
				.buildProcessInstanceRelatedInfo(processInstance)
				.buildProcessDefinitionRelatedInfo(processInstance.getProcessDefinition())
				.create();
		if (masterConfig.isKillYarnJobWhenTaskFailover()) {
			// only kill yarn job if exists , the local thread has exited
			LOGGER.info("TaskInstance failover begin kill the task related yarn job");
			ProcessUtils.killYarnJob(logClient, taskExecutionContext);
		}
	} else {
		LOGGER.info("The failover taskInstance is a master task");
	}
	taskInstance.setState(TaskExecutionStatus.NEED_FAULT_TOLERANCE);
	taskInstance.setFlag(Flag.NO);
	processService.saveTaskInstance(taskInstance);
        //提交event
	TaskStateEvent stateEvent = TaskStateEvent.builder()
			.processInstanceId(processInstance.getId())
			.taskInstanceId(taskInstance.getId())
			.status(TaskExecutionStatus.NEED_FAULT_TOLERANCE)
			.type(StateEventType.TASK_STATE_CHANGE)
			.build();
	workflowExecuteThreadPool.submitStateEvent(stateEvent);
}

event的提交会去根据其所属的工作流实例来选择其对应的WorkflowExecuteRunnable进行提交容错：

public void submitStateEvent(StateEvent stateEvent) {
	WorkflowExecuteRunnable workflowExecuteThread =
			processInstanceExecCacheManager.getByProcessInstanceId(stateEvent.getProcessInstanceId());
	if (workflowExecuteThread == null) {
		logger.warn("Submit state event error, cannot from workflowExecuteThread from cache manager, stateEvent:{}",
				stateEvent);
		return;
	}
	workflowExecuteThread.addStateEvent(stateEvent);
	logger.info("Submit state event success, stateEvent: {}", stateEvent);
}

处理容错event事件

在上面的代码中已经对需要容错的任务提交了一个event事件，那么肯定会有线程对这个event进行具体的处理。我们来看WorkflowExecuteRunnable类，submitStateEvent就是将event提交到了这个类中的stateEvents队列中：

private final ConcurrentLinked编程Queue<StateEvent> stateEvents = new ConcurrentLinkedQueue<>();

WorkflowExecuteRunnable在master启动的时候就已经启动了，并且会不停的从stateEvents中获取ev开发者_Go教程ent进行处理：

/**
 * handle event
 */
public void handleEvents() {
	if (!isStart()) {
		logger.info(
				"The workflow instance is not started, will not handle its state event, current state event size: {}",
				stateEvents);
		return;
	}
	StateEvent stateEvent = null;
	while (!this.stateEvents.isEmpty()) {
		try {
			stateEvent = this.stateEvents.peek();
			LoggerUtils.setWorkflowAndTaskInstanceIDMDC(stateEvent.getProcessInstanceId(),
					stateEvent.getTaskInstanceId());
			// if state handle success then will remove this state, otherwise will retry this state next time.
			// The state should always handle success except database error.
			checkProcessInstaandroidnce(stateEvent);
			StateEventHandler stateEventHandler =
					StateEventHandlerManager.getStateEventHandler(stateEvent.getType())
							.orElseThrow(() -> new StateEventHandleError(
									"Cannot find handler for the given state event"));
			logger.info("Begin to handle state event, {}", stateEvent);
			if (stateEventHandler.handleStateEvent(this, stateEvent)) {
				this.stateEvents.remove(stateEvent);
			}
		} catch (StateEventHandleError stateEventHandleError) {
			logger.error("State event handle error, will remove this event: {}", stateEvent, stateEventHandleError);
			this.stateEvents.remove(stateEvent);
			TpythonhreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
		} catch (StateEventHandleException stateEventHandleException) {
			logger.error("State event handle error, will retry this event: {}",
					stateEvent,
					stateEventHandleException);
			ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
		} catch (Exception e) {
			// we catch the exception here, since if the state event handle failed, the state event will still keep
			// in the stateEvents queue.
			logger.error("State event handle error, get a unknown exception, will retry this event: {}",
					stateEvent,
					e);
			ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
		} finally {
			LoggerUtils.removeWorkflowAndTaskInstanceIdMDC();
		}
	}
}

根据提交事件的类型StateEventType.TASK_STATE_CHANGE 可以获取到具体的StateEventHandler实现是TaskStateEventHandler。在TaskStateEventHandler的handleStateEvent方法中主要对需要容错的任务做了如下处理：

 if (task.getState().isFinished()) {
		if (completeTaskMap.containsKey(task.getTaskCode())
				&& completeTaskMap.get(task.getTaskCode()) == task.getId()) {
			logger.warn("The task instance is already complete, stateEvent: {}", stateEvent);
			return true;
		}
		workflowExecuteRunnable.taskFinished(task);
		if (task.getTaskGroupId() > 0) {
			logger.info("The task instance need to release task Group: {}", task.getTaskGroupId());
			workflowExecuteRunnable.releaseTaskGroup(task);
		}
		return true;
	}

其中判断是否完成的具体实现中就包含了是否是容错的状态。

public boolean isFinished() {
	return isSuccess() || isKill() || isFailure() || isPause();
}
public boolean isFailure() {
	return this == TaskExecutionStatus.FAILURE || this == NEED_FAULT_TOLERANCE;
}

接着就会调用workflowExecuteRunnable.taskFinished(task);方法去处理各种任务实例状态变化后的事件。这里我们只关注容错相关的代码分支：

} else if (taskInstance.taskCanRetry() && !processInstance.getState().isReadyStop()) {
			// retry task
			logger.info("Retry taskInstance taskInstance state: {}", taskInstance.getState());
			retryTaskInstance(taskInstance);
}
//判断了是否容错的状态，前面对其已经进行了更新
public boolean taskCanRetry() {
	if (this.isSubProcess()) {
		return false;
	}
	if (this.getState() == TaskExecutionStatus.NEED_FAULT_TOLERANCE) {
		return true;
	}
	return this.getState() == TaskExecutionStatus.FAILURE && (this.getRetryTimes() < this.getMaxRetryTimes());
}
/**
 * crate new task instance to retry, different objects from the original
 *
 */
private void retryTaskInstance(TaskInstance taskInstance) throws StateEventHandleException {
	if (!taskInstance.taskCanRetry()) {
		return;
	}
	TaskInstance newTaskInstance = cloneRetryTaskInstance(taskInstance);
	if (newTaskInstance == null) {
		logger.error("Retry task fail because new taskInstance is null, task code:{}, task id:{}",
				taskInstance.getTaskCode(),
				taskInstance.getId());
		return;
	}
	waitToRetryTaskInstanceMap.put(newTaskInstance.getTaskCode(), newTaskInstance);
	if (!taskInstance.retryTaskIntervalOverTime()) {
		logger.info(
				"Failure task will be submitted, process id: {}, task instance code: {}, state: {}, retry times: {} / {}, interval: {}",
				processInstance.getId(), newTaskInstance.getTaskCode(),
				newTaskInstance.getState(), newTaskInstance.getRetryTimes(), newTaskInstance.getMaxRetryTimes(),
				newTaskInstance.getRetryInterval());
		stateWheelExecuteThread.addTask4TimeoutCheck(processInstance, newTaskInstance);
		stateWheelExecuteThread.addTask4RetryCheck(processInstance, newTaskInstance);
	} else {
		addTaskToStandByList(newTaskInstance);
		submitStandByTask();
		waitToRetryTaskInstanceMap.remove(newTaskInstance.getTaskCode());
	}
}

最终将需要容错的任务实例重新加入到了readyToSubmitTaskQueue队列中，重新进行submit：

addTaskToStandByList(newTaskInstance);
submitStandByTask();

后面就是和正常任务一样处理了通过submitTaskExec方法提交任务到具体的worker执行。

总结

对于Worker的容错流程大致如下：

1-Master基于ZK的监听来感知需要容错的Worker节点信息

2-每个Master只负责容错属于自己调度的工作流实例，在容错前会比较实例的开始时间和服务节点的启动时间，在服务启动时间之后的则跳过容错；

3-需要容错的任务实例会重新加入到readyToSubmitTaskQueue，并提交运行。

到此，对于Worker的容错，就到这里了，更多关于DolphinScheduler容错Worker的资料请关注我们其它相关文章！

继续阅读：DolphinScheduler Worker DolphinScheduler容错Worker

DolphinScheduler容错源码分析之Worker

目录

引言

Worker容错源码分析

worker启动注册

Master监听worker在zk节点的状态

处理容错event事件

总结

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